Blog Archives

Update on inference with Wasserstein distances

August 15, 2017
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Update on inference with Wasserstein distances

Hi again, As described in an earlier post, Espen Bernton, Mathieu Gerber and Christian P. Robert and I are exploring Wasserstein distances for parameter inference in generative models. Generally, ABC and indirect inference are fun to play with, as they make the user think about useful distances between data sets (i.i.d. or not), which is sort of implicit in classical […]

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Unbiased MCMC with couplings

August 14, 2017
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Unbiased MCMC with couplings

    Hi, With John O’Leary and Yves Atchadé , we have just arXived our work on removing the bias of MCMC estimators. Here I’ll explain what this bias is about, and the benefits of removing it. What bias? An MCMC algorithm defines a Markov chain , with stationary distribution , so that time averages of the chain […]

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Particle methods in Statistics

June 30, 2017
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Particle methods in Statistics

Hi there, In this post, just in time for the summer, I propose a reading list for people interested in discovering the fascinating world of particle methods, aka sequential Monte Carlo methods, and their use in statistics. I also take the opportunity to advertise the SMC workshop in Uppsala (30 Aug – 1 Sept), which […]

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Likelihood calculation for the g-and-k distribution

June 10, 2017
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Likelihood calculation for the g-and-k distribution

    Hello, An example often used in the ABC literature is the g-and-k distribution (e.g. reference [1] below), which is defined through the inverse of its cumulative distribution function (cdf). It is easy to simulate from such distributions by drawing uniform variables and applying the inverse cdf to them. However, since there is no closed-form […]

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ABC in Banff

March 6, 2017
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ABC in Banff

Hi all, Last week I attended a wonderful meeting on Approximate Bayesian Computation in Banff, which gathered a nice crowd of ABC users and enthusiasts, including lots of people outside of computational stats, whom I wouldn’t have met otherwise. Christian blogged about it there. My talk on Inference with Wasserstein distances is available as a video here (joint […]

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Statistical inference with the Wasserstein distance

January 26, 2017
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Statistical inference with the Wasserstein distance

Hi! It’s been too long! In a recent arXiv entry, Espen Bernton, Mathieu Gerber and Christian P. Robert and I explore the use of the Wasserstein distance to perform parameter inference in generative models. A by-product is an ABC-type approach that bypasses the choice of summary statistics. Instead, one chooses a metric on the observation space. […]

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Coupling of particle filters: smoothing

July 20, 2016
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Coupling of particle filters: smoothing

    Hi again! In this post, I’ll explain the new smoother introduced in our paper Coupling of Particle Filters with Fredrik Lindsten and Thomas B. Schön from Uppsala University. Smoothing refers to the task of estimating a latent process of length , given noisy measurements of it, ; the smoothing distribution refers to . The setting is state-space […]

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Coupling of particle filters: likelihood curves

July 19, 2016
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Coupling of particle filters: likelihood curves

Hi! In this post, I’ll write about coupling particle filters, as proposed in our recent paper with Fredrik Lindsten and Thomas B. Schön from Uppsala University, available on arXiv; and also in this paper by colleagues at NUS. The paper is about a methodology with multiple direct consequences. In this first post, I’ll focus on correlated likelihood estimators; in a later […]

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Sequential Bayesian inference for time series

May 19, 2015
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Sequential Bayesian inference for time series

Hello hello, I have just arXived a review article, written for ESAIM: Proceedings and Surveys, called Sequential Bayesian inference for implicit hidden Markov models and current limitations. The topic is sequential Bayesian estimation: you want to perform inference (say, parameter inference, or prediction of future observations), taking into account parameter and model uncertainties, using hidden Markov […]

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Sequential Bayesian inference for time series

May 19, 2015
By
Sequential Bayesian inference for time series

Hello hello, I have just arXived a review article, written for ESAIM: Proceedings and Surveys, called Sequential Bayesian inference for implicit hidden Markov models and current limitations. The topic is sequential Bayesian estimation: you want to perform inference (say, parameter inference, or prediction of future observations), taking into account parameter and model uncertainties, using hidden Markov […]

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